class_docstr: Calculates the average loss according to the passed loss_fn.
  
  Args:
      loss_fn: a callable taking a prediction tensor, a target
          tensor, optionally other arguments, and returns the average loss
          over all observations in the batch.
      output_transform: a callable that is used to transform the
          :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
          form expected by the metric.
          This can be useful if, for example, you have a multi-output model and
          you want to compute the metric with respect to one of the outputs.
          The output is expected to be a tuple `(prediction, target)` or
          (prediction, target, kwargs) where kwargs is a dictionary of extra
          keywords arguments. If extra keywords arguments are provided they are passed to `loss_fn`.
      batch_size: a callable taking a target tensor that returns the
          first dimension size (usually the batch size).
      device: specifies which device updates are accumulated on. Setting the
          metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
          non-blocking. By default, CPU.
  
  Attributes:
      required_output_keys: dictionary defines required keys to be found in ``engine.state.output`` if the
          latter is a dictionary. Default, ``("y_pred", "y", "criterion_kwargs")``. This is useful when the
          criterion function requires additional arguments, which can be passed using ``criterion_kwargs``.
          See an example below.
  
  Examples:
      Let's implement a Loss metric that requires ``x``, ``y_pred``, ``y`` and ``criterion_kwargs`` as input
      for ``criterion`` function. In the example below we show how to setup standard metric like Accuracy
      and the Loss metric using an ``evaluator`` created with
      :meth:`~ignite.engine.create_supervised_evaluator` method.
  
      For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`.
  
      .. include:: defaults.rst
          :start-after: :orphan:
  
      .. testcode::
  
          model = default_model
          criterion = nn.NLLLoss()
          metric = Loss(criterion)
          metric.attach(default_evaluator, 'loss')
          y_pred = torch.tensor([[0.1, 0.4, 0.5], [0.1, 0.7, 0.2]])
          y_true = torch.tensor([2, 2]).long()
          state = default_evaluator.run([[y_pred, y_true]])
          print(state.metrics['loss'])
  
      .. testoutput::
  
          -0.3499999...
code: def reset(self) -> None:
      self._sum = torch.tensor(0.0, device=self._device)
      self._num_examples = 0
  
file_path: ignite/ignite/metrics/loss.py
imports: from typing import Callable, cast, Dict, Sequence, Tuple, Union
  import torch
  from ignite.exceptions import NotComputableError
  from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce
label_desc: Use image node_id 2 for calling the Loss obj's underlying member method code with example usage: obj.reset() without return types